The Patient Health Questionnaire-9 (PHQ-9) has been widely used to screen depression symptoms. The present research aimed to assess the reliability and validity of PHQ-9, besides measurement invariance of the PHQ-9 across gender and age among Chinese university students. A total of 12,957 Chinese college students from 2 universities in Henan and Hainan provinces (China) completed the questionnaires via WeChat. This research reported the psychometric properties of PHQ-9 and measurement invariance of the PHQ-9 across gender and age among Chinese university students. Compared with 1-factor model, the 2-factor (affective factor and somatic factor) model of PHQ-9 showed a better fit index in Chinese university students. Without the last 2 items, the 2-factor model of the PHQ-9 showed satisfactory reliability, validity, and good fit index . The Cronbach’s alpha of PHQ-9 was 0.874. Multi-group analysis across gender and age demonstrated that measurement equivalency for the 2-factor model of the PHQ-9 was established . The 2-factor model of the PHQ-9 without the items of “movement” and “desire to die” showed a better fit index in Chinese university students. The measurement equivalence across gender and age for the 2-factor model of the PHQ-9 can be established among Chinese university students. Depression is a consequence of complex interactions between biological, social and psychological factors. Studies have shown that in recent years, the incidence of depression has been increasing worldwide year by year.–4 At present, several scoring systems have been utilized to measure depressive symptoms, while the majority of them include more than 10 items and may be inconsistent with the diagnostic criteria for depressive disorders.,6 Therefore, other effective and brief scoring systems need to be developed to screen depression symptoms among university students.The World Health Organization (WHO) defined depression as the fourth global leading cause of disability, accounting for 3.7% of all disability-adjusted life years.,8 A number of scholars have demonstrated that PHQ-9 can effectively screen depressive symptoms, accompanied by satisfactory sensitivity and specificity.–11 PHQ-9 can assess somatic and non-somatic symptoms of depression, and even the risk of suicide. The PHQ-2, consisting of the first 2 items of the PHQ-9, is a well-studied screening measure of depression often used in primary care. There are 2 cardinal symptoms of major depression, sometimes referred to as “gateway” symptoms, because at least 1 must be present for the patient to meet the diagnostic criteria for the illness. These symptoms are depressed mood and diminished interest or pleasure in all, or nearly all, activities (anhedonia). These 2 cardinal symptoms are assessed by the PHQ-2.The Patient Health Questionnaire (PHQ) is a new instrument for making criteria-based diagnoses of depression and other mental disorders commonly encountered in primary care. The patient health questionnaire-9 (PHQ-9) is the depression module, which scores each of the 9 DSM-IV  criteria as “0”  to “3” (nearly every day).,13 Meanwhile, PHQ-9 has been utilized to discriminate depression symptoms of the general population effectively, and it also has been employed to assess depression symptoms to explore the underlying psychological and behavioral mechanisms. Previous research demonstrated that PHQ-9 is of great significance in estimating depression symptoms amongst Ethiopian, Nigerian, Colombian, Korean, and Chinese university students. The Chinese version of the PHQ-9 was applied to screen depressive symptoms in the Chinese population, which showed satisfactory sensitivity and specificity.,20 For university students, PHQ-9 was used to screen their depressive symptoms.Several scholars applied PHQ-9 to screen depressive symptoms in patients with and without mental health problems. Evidence on the measurement invariance of PHQ-9 according to sex is less consistent than in other group comparisons. Studies supported a strong invariance for sex comparisons, whereas other studies reported weak or no measurement invariance across sexes, indicating that men and women interpret the PHQ-9 items differently.Several scholars have shown that depression may associate with gender and age, while females and young adults have shown a stronger correlation with factors of gender and age, respectively. A number of researchers assessed the measurement invariance of the standard 1-factor model of the PHQ-9 and 5 previously identified 2-factor models for 4443 American Indian and 4443 Caucasian American adults (age ≥ 18 years old) with a PHQ-9. All models showed good fits  > 0.99, root mean square error of approximation (RMSEA) < 0.05) and internal consistency reliability . All models displayed measurement invariance between racial groups. Factor correlation was high for 2-factor models, providing support for the 1-factor model. American Indian adults had significantly higher odds of PHQ-9 total scores ≥ 10 and ≥ 15 than Caucasian American adults. A recent study examined measurement invariance for the PHQ-9 by participants’ sex, recruitment stratum, and linguistic background. Results supported the invariance of PHQ-9 across sex, patient strata and linguistic background. The adequate psychometric properties for PHQ-9 allow direct multi-group comparison across sex, strata, and linguistic background. PHQ-9 has a 2-factor structure for persistent major depressive disorder patients, with solid measurement invariance between treatment groups at and across follow-up time. However, previous research also reported that PHQ-9 was not measurement invariant across Germans without a migration background and asylum seekers living in Germany; even with the same latent level of depression, asylum seekers may have higher scores on several items and consequently a higher sum score. The present research aims to assess the reliability and validity of PHQ-9. The factor structure and measurement invariance of the PHQ-9 across sex and age among Chinese university students are also performed.The PHQ-9 shows measurement invariance totally for women and partially for men in a study of a Dutch sample, particularly for the item “psychomotor problems.2.1.A total of 12,957 Chinese university students from 1 university in Henan and Hainan provinces (China) completed the questionnaires via WeChat. Except for 99 university students who provided incomplete data anonymously, 12,858 students  were included in the current survey. The age of the participants ranged from 17 to 25 years old .All participants participated in this study voluntarily and signed the online informed consent before participation. Ethical approval for this study was obtained from Xinxiang Medical University Ethics Committee (HYLL2020005). All questionnaires were administered on the public account platform of WeChat. The psychology teacher instructed the students to finish the basic information and complete the questionnaire online. At the same time, all subjects were able to obtain timely feedback on their depression status after completing the measurement.2.2. Subjects rated the frequency of the 9 symptoms over the past 2 weeks on a 4-point Likert scale . The total score of the PHQ-9 ranged from 0 to 27, and higher scores indicated greater severity of depressive symptoms. According to the Chinese version of the PHQ-9, 0-4 is normal, 5 to 9 is mild, 10-14 is moderate, 15 to 19 is moderate severe, and 20 to 27 is severe depression symptoms.PHQ-9 includes 9 items and is usually utilized to assess the severity of depressive symptoms by self-reporting. The 9 items were consistent with the DSM-IV criteria for major depressive disorder, and 2 subscales of somatic and non-somatic symptoms were involved. The related research indicated that PHQ-9 showed Cronbach’s of 0.89 among patients.2.3.The data were analyzed using SPSS Statistics version 23  and AMOS version 21. Results were presented as mean ± standard deviation. According to the symptom scores of PHQ-9, the basic depression status of university students was analyzed. For further analysis, the distribution of the data was analyzed as well.In order to explore the factor structure, our analysis included 2 steps. Firstly, exploratory factor analysis (EFA) was conducted on a randomly split-half of the whole sample (n = 6386) to identify the best fitting factor model of the PHQ-7 in university students. Secondly, Confirmatory factor analysis (CFA) was performed using the remaining sample (n = 6472). Model fit was assessed with various fit indices, including Chi-square with estimated degree of freedom, CFI, Tucker-Lewis index (TLI), and RMSEA. RMSEA < 0.08 and CFI and TLI > than 0.90 indicated acceptable models. construct reliability (CR) and average variance extracted (AVE) were calculated. After the identification of the best fitting model, measurement invariance tests were conducted across gender and age to assess the effectiveness of inter-group comparisons. Five aspects of invariance were considered, including the unconstrained model, measurement weights model, measurement intercepts model, structural covariances model and measurement residuals model. First, the unconstrained model is the basic model of equivalence, which refers to the pattern equivalence of the model. Based on pattern invariance, the factor loading invariance and measurement intercept of different groups are limited to be equivalent. Finally, based on the above 3 models, this study further explored whether the covariance and residuals are the same across different groups. The CFI, TLI and RMSEA were used to evaluate invariance across consecutive models; besides, in the case of CFI and TLI ≥ 0.9, measurement invariance was accepted, and CFI, TLI, and RMSEA changes were employed to evaluate invariance; ΔCFI ≤ 0.01, ΔTLL ≤ 0.01, and ΔRMSEA ≤ 0.015 were considered evidence of invariance.Based on the good factor structure, the reliability of the PHQ-9 was estimated with Cronbach’s alpha. In the case of Cronbach’s alpha > 0.5,3.1.t = 3.082, P = .002) . According to the cutoff value of the PHQ-9, 30.2% of the participants had depression (PHQ-9 score ≥ 5), including 8971 (69.8%) with normal, 2967 (23.1) with mild, 630 (4.9%) with moderate, 201 (1.6%) with moderately severe, and 89 (0.7%) with severe depression symptoms. The majority of the items were normally distributed, except for the last 2 items, “movement” and “desire to die.” There were 85 (0.7%) and 31 (0.3%) students with severe “movement” and “desire to die,” respectively.In the whole sample, the mean PHQ-9 score was 3.26 ± 4.12 for males and 3.49 ± 3.90 for females. Gender-based differences were statistically significant . Based on the recent research and the large sample sizes in our study, the factor extraction criteria is 0.5. Except for items of “movement” and “desire to die,” all factor loading of all items were more than 0.5. The standard 2-factor accounted cumulatively for 52.64% of the total variance. EFA factors and factor loadings are presented in Table  The normality test showed that the skewness of all items were less than 3, and the kurtosis of all items were less than 10, except item 8 and 9, which demonstrated all other 7 items conformed to the normal distribution and were suitable for future data analysis. Followed-up analysis excepted the last 2 items due to the low frequency and unnormal distribution.Since the factor structure of PHQ-9 was controversial, EFA was utilized to determine the applicability of the 2- or 1-factor structure of PHQ-9.χ2 (13) = 325.729, P = .000, CFI = 0.984, Goodness-of-fit index = 0.986, TLI = 0.974, and RMSEA = 0.060   = 0.052 . Each fit index met the requirements of configural invariance for the 1-factor model of the PHQ-9 , and ΔCFI and ΔTLI were equal to 0.002  had significantly more depressive symptoms. Compared with younger university students, older university students experienced a higher pressure of study and employment. The management of mental health disorders among university students may be realized by alleviating university students’ psychological problems. However, due to academic stress, The factor structure of PHQ-9 has remained a controversial topic. EFA results showed that the 2-factor structure of PHQ-9 is more appropriate for university students rather than the 1-factor structure. This result is also consistent with the finding of previous research.The PHQ-9 is mainly utilized to evaluate depressive symptoms, while it may include somatic and non-somatic symptoms. Compared with other symptoms, the first 2 items of the PHQ-9 also showed higher factor loading, which was consistent with outcomes of previous research.,19 However, many university students reported mild or moderate depression symptoms, and a low proportion of university students had reported movement and suicidal thoughts.The results of CFA revealed that the structural validity of the PHQ-9 was acceptable, and the items of the PHQ-9 could effectively represent latent variables. Compared with the clinical psychiatric sample, the factor loading of the university sample was relatively lower as most university students did not report severe depressive symptoms. The Cronbach’s alpha in the PHQ-9 was consistent with previously reported research that presented outstanding reliability in mental health patients, non-mental health patients,,39 and the general population.,20,40 According to the current survey results, the 2-factor model of the PHQ-9 is highly recommended for university students.Without reaching acceptable reliability and validity, using the PHQ-9 may result in under-detection or over-detection of depression symptoms in certain groups. the equivalence of PHQ-9 was established across age and gender. However, the reliability and validity were not reported in that research. With different age groups and different cultural backgrounds, the scale may only be partially applicable. Therefore, this study further explored the reliability, validity and equivalence of PHQ-9 in Chinese university students. Similar to younger Chinese adolescents, measurement invariances of the PHQ-9 across gender- and age-based subgroups in Chinese university students were also confirmed. In this research, the 2-factor model of the PHQ-9 met the requirements for configural equivalence. Therefore, the next step of the equivalence test could be carried out. The establishment of the measurement weight invariance indicated that the observed and potential traits of the PHQ-9 have the same meaning in different gender- and age-based groups. Therefore, the latent variables of the PHQ-9 and the meaning of the observed items are equivalent in different groups. Hence, the intercept of indicators and structural co-variances were further analyzed. The results of the current survey revealed that the intercept of indicators and structural co-variances were satisfied. This finding also was consistent with previously reported outcomes.For the superior reliability and validity of PHQ-9 without the last 2 items, we only analyzed the measurement equivalency of PHQ-9 in the current research. Because of the large sample size of this research, chi-square values were easily affected by the sample size, which may lead to significant differences. Although relevant research also explored the equivalence of PHQ-9 among middle school students of Hong Kong,There are some limitations in the present research. This research collected data from 3 medical universities in 2 provinces of China, while there might be some differences in depression symptoms of students from different universities. Therefore, in the next study, the level of education and type of school should be considered. Additionally, self-report assessment may also have some shortcomings. Therefore, other methods investigating depression symptoms can be used in combination to reach higher clinical efficacy. In addition, retest reliability may be beneficial to explore the stability, and other validity, such as convergent or discriminating validity, should be considered to further conduct for better certification of the measurement properties.Compared with 1-factor model, the 2-factor (affective factor and somatic factor) model of PHQ-9 without the items of “movement” and “desire to die” showed a better fit index in Chinese university students. The 2-factor structure model was found highly appropriate for university students and indicated satisfactory reliability and validity. The measurement equivalence across different genders and ages was established using the 2-factor model of the PHQ-9 for Chinese university students among Chinese university students.This study was funded by National Science Fund for Distinguished Young Scholars (81725005 to Fei Wang), National Natural Science Foundation Regional Innovation and Development Joint Fund (U20A6005 to Fei Wang), Jiangsu Provincial Key Research and Development Program (BE2021617 to Fei Wang), Inner Mongolia Autonomous Region Postgraduate Education Innovation Program Funding Project (B202101194Z to Yang Wang), Hainan Provincial Natural Science Foundation of China (821RC700 to Lijuan Liang), Henan Province Higher Education Teaching Reform Research and Practice Project (2021SJGLX189 to Yongxun Liu).Conceptualization: Lijuan Liang, Shisan Qi, Fei Wang.Data curation: Yang Wang.Formal analysis: Yang Wang.Funding acquisition: Yang Wang, Rongxun Liu, Shisan Qi, Fei Wang.Investigation: Lijuan Liang, Rongxun Liu, Yange Wei, Fei Wang.Methodology: Lijuan Liang, Qiao Ke.Project administration: Qiao Ke.Resources: Yange Wei.Software: Yang Wang, Yange Wei.Supervision: Yang Wang, Zhenyuan Sun, Qiao Ke.Validation: Lijuan Liang, Rongxun Liu.Visualization: Lijuan Liang.Writing – original draft: Lijuan Liang.Writing – review & editing: Yang Wang, Zhenyuan Sun, Yange Wei, Fei Wang.


We replicated the results of Wu et al. and extended the analysis up until May 2022. The association between PM2.5 concentration and cumulative COVID-19 mortality fell sharply after September 2020. Using the data available from Wu et al.’s “updated_data” branch up until May 2022, we found that the effect of a 1 μg/m3 increase in PM2.5 was associated with only a +0.603% mortality difference. The 95% CI of this difference was between −0.560% and +1.78%, narrow bounds that include zero, with the upper bound far below the Wu et al. estimate. Short-term trends in the initial spread of COVID-19, not a long-term epidemiologic association, caused an early correlation between air pollution and COVID-19 mortality.Wu et al. found a strong positive association between cumulative daily county-level COVID-19 mortality and long-term average PM It is of critical importance for public health to understand the correlation between vulnerability to COVID-19 infection and mortality. Early studies showed a high correlation between air pollution and a host of social vulnerabilities. We decided to replicate an early study of this relationship to further explore the study’s implications. However, after we replicated the study, we found that the correlation between air pollution and COVID-19 mortality appears to have been happenstance based on the early period of the pandemic. Other authors could consider whether the correlation of COVID-19 mortality with other social vulnerabilities has persisted throughout the pandemic.2.5 and new daily confirmed COVID-19 cases” and that “daily maximum eight-hour ozone concentration” is positively associated with “new confirmed cases” but not “new deaths” [Based on the early outbreak of COVID-19 cases in Northern Italy, Conticini et al. . In the  deaths” . Bourdre deaths”  suggeste3 in the long-term average PM2.5 was associated with an 11% higher COVID-19 mortality rate (95% CI: 6% to 17%). Wu et al. used the average county-level PM2.5 concentration from 2000 to 2016 (a 17-year average) as the predictive variable. The US EPA standard for PM2.5 is 12.0 μg/m3, and at 609 monitored sites in 2020, the average value was 8.0 with 10th and 90th percentile average values of 5.6 and 10.8 across sites [3 of long-term average PM2.5 exposure using a negative binomial regression at the county level for cumulative mortality for each day of the pandemic  through September 2020.Wu et al. ,9 analyz3 of long-term average PM2.5 exposure had a 10% higher mortality rate . The mortality rate ratio from the regression peaked around 1.11 in mid-June 2020 and remained just under 1.10 in September 2020. Wu et al. found the association of air pollution with COVID-19 mortality while holding a wide range of explanatory variables associated with social vulnerability constant, including racial/ethnic composition, poverty, education, age structure, temperature, relative humidity, available hospital beds, population density, obesity, and smoking, by controlling for these variables.Take an example from the middle of the time-period to illustrate the cumulative mortality rates. The value of 1.10 for early June 2020 indicated that counties with one additional μg/mWe replicated Wu et al.’s results exactly. Our results matched the results of Wu et al. exactly. Thus, we successfully replicated the method and the finding of a stable, positive association between air pollution and COVID-19 mortality over the period analyzed by Wu et al.: from April to September 2020.2.5 concentrations from 2000 to 2016 that Wu et al. used. We also used the same array of potential confounding variables as Wu et al. We purposely used the exact same methods except that we used a longer time frame to estimate COVID-19 mortality, to show that only this change affected the results. As we analyzed the data cumulatively from the onset of the pandemic through each date on the horizontal axis, We then extended the analysis to determine if the relationship between air pollution and COVID-19 mortality persists after the study period in Wu et al. In 3 increase in PM2.5 exposure.Each value is the mortality rate ratio for cumulative county mortality associated with a one μg/m2.5 air pollution and COVID-19 mortality after September 2020. The mortality rate ratio declined sharply and never again rose above 1.03. It is worth noting that the extremely deadly second wave of US COVID-19 coincides closely with the decrease in the relationship between COVID-19 mortality and air pollution. Additionally, vaccines only became available in early 2021, so vaccination cannot explain the decline that began in September. See The most striking feature of The disappearance of the association between air pollution and COVID-19 mortality suggests that the association was particular to one phase (the first phase) of the pandemic and does not represent an important causal relationship between air pollution and COVID-19 mortality. Wu et al. were careful not to use causal language in their analysis. However, the Harvard School of Public Health did use causal language in their press release: “more evidence of causal link between air pollution and early death” .2) over the first year of the pandemic between several important demographic variables and the COVID-19 mortality rate for the previous 30 days. We used only the data from the previous month to separate short-term trends in the spread of COVID-19 from long-term trends in COVID-19 mortality. COVID-19 deaths increased in counties at uneven rates. For example, COVID-19 deaths increased disproportionately the fastest in Hispanic communities in early August. Between September 2020 and December 2020, the correlations among all five demographic variables, including PM2.5, changed significantly as COVID-19 spread into new communities.Though Wu et al. used the best available data at the time, they found a short-term trend in the spread of COVID-19, not a long-term trend in COVID-19 mortality. To clarify, we are not referring to short-term and long-term exposure to air pollution. In None of these correlations are causally related to long-term COVID-19 mortality. Even though these are short-term relationships between demographic variables and the spread of COVID-19, the relationships are neither fair nor accidental. For example, it is certainly neither fair nor accidental that COVID-19 affected Black communities disproportionately more at the beginning of the pandemic: Black Americans are disproportionately more likely to live in urban areas, which were the locations where COVID-19 first spread. Moreover, in addition to short-term relationships between these variables and the spread of COVID-19, there may be additional long-term relationships between these variables and COVID-19 mortality. Nevertheless, the relationships outlined above represent short-term trends in the spread of COVID-19, not long-term trends in COVID-19 mortality.The association between COVID-19 mortality and air pollution was limited to the earliest phase and, we conclude, to the particular timing and location of the early COVID-19 outbreak. This is despite social inequalities exacerbating the toll of the pandemic, and the long-term inequality such as the differential exposure of vulnerable populations to air pollution .2.5 concentrations, there is no long-term relationship between COVID-19 mortality and PM2.5 concentrations. Perhaps Wu et al. omitted an important variable, and this caused them to find a short-term relationship between the spread of COVID-19 and PM2.5 concentrations through September 2020. For example, imagine that long-distance travelers spread COVID-19 early during the pandemic. Long-distance travelers can also directly emit PM2.5 from their vehicles. Wu et al. might have falsely attributed unexplained differences in COVID-19 mortality to PM2.5 concentrations instead. When COVID-19 spread into counties with fewer long-distance travelers, these unexplained differences and the effect Wu et al. estimated decreased. However, we have not found any evidence for this explanation. Regardless of the specific reason for the short-term relationship between the spread of COVID-19 and PM2.5 concentrations, there is no long-term relationship between COVID-19 mortality and PM2.5 concentrations.Regardless of the specific reason for the short-term relationship between the spread of COVID-19 and PMOne could view these results as part of the so-called “replication crisis”. Ioannidis argued that “most published research findings are false” and that “a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance” .Perhaps instead of viewing this as a replication “crisis”, we should view this as a replication “challenge”, for researchers to replicate more studies on new populations and for academic institutions and publishers to reward research that is not necessarily “first out of the gate”.2.5 and COVID-19 by modeling COVID-19 as a pandemic [Because we used the same methodology as Wu et al., our study has the same limitations as theirs. An important and inherent limitation of ecological regression is that authors cannot guarantee that they have included all potential confounding variables. Wu et al. suggested that future authors should “quantify and correct for ecological bias and measurement error”, use “reproducible methods for causal inference”, and quantify “measured and unmeasured confounding bias”. Additionally, multiple regression is an inaccurate tool for modeling the spread of COVID-19 because COVID-19 infections are not confined by space and time. At the time that the article by Wu et al. was written, obtaining enough data for a diffusion model might have been difficult. However, as time passes, one can obtain more and more data on the spread of COVID-19, and some authors have indeed studied the relationship between PMpandemic ,13. Idea2.5 concentrations over a long time-period prior to the pandemic, during and after which many people may have moved. This average may be a good indicator of long-term exposure, but not of exposure during the pandemic. When researchers estimate the effects of air pollution on all-cause mortality, they generally find that all-cause mortality increases more when exposure times are longer [In the future, authors could consider how the duration of air-pollution exposure might (or might not) influence COVID-19 mortality rates. Wu et al. averaged PMe longer . Some aue longer . Howevere longer .2.5 concentrations and COVID-19 mortality rates early during the pandemic, evidence for a causal relationship is limited. While data through September 2020 suggest that higher air pollution increased COVID-19 death rates, the same methods applied to data throughout mid-2022 showed little association between air pollution and COVID-19 mortality rates. Short-term trends in the initial spread of COVID-19, not a long-term epidemiologic association, caused an early correlation between air pollution and COVID-19 mortality.In conclusion, while we observed a strong positive relationship between PM


The free energy for the TYP-ATR complexes was negative, suggesting a spontaneous interaction. Thermodynamic parameters enthalpy (ΔH) and entropy (ΔS) obtained positive values for the TYP-ATR system suggesting hydrophobic interactions in the binding process. Micro-environmental and conformational changes in TYP molecules were induced on interaction with ATR. Reduced catalytic activity of TYP was observed after interaction with ATR owing to the changes in the secondary structure of the TYP.Atrazine (ATR) is a herbicide globally used to eliminate undesired weeds. Herbicide usage leads to various adverse effects on human health and the environment. The primary source of herbicides in humans is the food laced with the herbicides. The ATR binding to trypsin (TYP) was investigated in this study to explore its binding potential and toxicity. In vitro interaction of ATR with TYP was studied using multi-spectroscopic methods, molecular docking, and enzyme kinetics to explore the mechanism of binding for the TYP-ATR system. The TYP-ATR complex revealed binding constants (10 Herbicide usage is widely practiced in crops to eliminate unwanted plants and weeds, thus essential in food production sustainability ,2. HowevIn recent years, proteins have been the main investigational target to study the toxicity of environmental pollutants, especially pesticides, which induce changes in the protein’s structural (conformation) and biological function ,20,21. PTrypsin (bovine pancreas) ATR, N-α-Benzoyl-D, L-arginine-P-nitroanilide hydrochloride in (BApNA) were all obtained from Sigma-Aldric Co. . Analytical grade solvent and chemicals purchased locally were used in the study.2HPO4), monobasic (NaH2PO4), and NaCl) stock solution of ATR (0.5 mM) in anhydrous methanol and BApNA (3 mM) was prepared in DMSO. All the reagents were stored at 2–8 °C until use. Buffers were prepared in ultra-pure water.The TYP solution (0.25 mM) was prepared in phosphate-buffered saline . The fluorescence measurements were recorded with an RF-5301PC spectrofluorometer . The circular dichroism (CD) measurements were recorded on a JASCO J-1500CD spectrophotometer. FT-IR measurements were carried out on Avatar 360 E.S.P. FT-IR spectrometer The UV-vis spectra of TYP were recorded in the presence of ATR (0–30 µM). Experiments were performed at fixed TYP concentration (10 µM).Fluorescence emission spectra of TYP (10 μM) titrated against ATR (0–45 µM) at 298, 303, and 308 K were recorded. The emission wavelength range was set to 300–500 nm after excitation at 280 nm.Synchronous fluorescence spectra measurements of TYP (10 μM) titrated with increasing concentrations of ATR (0–45 µM) were recorded in separate experiments by setting wavelength interval (Δλ) at 15 nm (for tyrosine residues (Y)) and 60 nm (for tryptophan residues (w)) in the same experimental conditions as the steady-state fluorescence emission spectra.−1 and 1 nm bandwidth. The recorded spectrum was analyzed by the web-based software package tool, CAPITO-CD Analysis and Plotting Tool , to evaluate different secondary structures of TYP after being treated with ATR.The CD spectra of TYP (5 μM) with ATR (0 and 10 μM) were recorded from 190–260 nm with a scan speed of 20 nm min−1. The corresponding absorbance of buffer and ATR solutions recorded under similar conditions was subtracted from the spectra.The FTIR spectra for TYP were recorded with ATR. The FTIR spectroscopy of free TYP (20 μM) and TYP (20 μM)-ATR (100 μM) were recorded in the range of 1800–1400 cmhttp://www.rcsb.org/pdb, accessed on 3 March 2022). Molecular modeling of the ATR−TYP complexation was performed using AutoDock Vina. For docking purposes, water, metal ions, and the initially bound ligand were removed, and hydrogen atoms were added during protein preparation. The pdb structure of ATR was obtained from Pubchem (ID: 2256), and its energy-minimized conformation was analyzed using Chem3D Pro14.0. During the docking study, a grid box defined to enclose the entire binding site of trypsin with dimensions of 40 Å × 40 Å × 40 Å and a grid spacing of 0.375 Å was used. The grid center of the TYP-ATR system was set at −26.319, 1.973, and 16.726. The docked structure of the TYP-ATR system was analyzed with Discovery studio 4.5.The molecular structure of TYP (PDB ID: 2ZQ1) was obtained from Protein Data Bank (RCSB), The activity of TYP was measured using BApNA as a substrate according to the reported literature ,29,30. Tmax, denote the initial and final reaction velocities, Km is Michaelis Constant, and Kcat is the catalytic rate constant. Whereas [E] represents enzyme concentration and [S]: substrate concentration (BApNA). Lineweaver-Burk plots for 1/V vs. 1/[S] at different BApNA concentrations in the absence and presence of ATR.The above equations, V and VUV-vis absorption spectroscopy is a simple and versatile technique for studying the conformational and structural changes in a protein and also explores the protein-ligand complex formation . The UV-Moreover, the absorbance intensity at 275 nm also decreased, which confirms that the polarity of the microenvironment around the aromatic amino acid residues was affected by the ATR presence . TherefoFluorescence emission spectroscopy is a technique to study the protein-ligand interactions and conformational alterations ,35. In t0 represents the steady-state fluorescence intensity of TYP, F fluorescence intensity of TYP in the presence of ATR, KSV is the Stern-Volmer quenching constant, and [Q] is the quencher concentration.The fluorescence quenching mechanisms of protein are classified into three mechanisms: static, dynamic, and mixed (static and dynamic) quenching . The queSV values with respect to temperature represents the static quenching mechanism for the TYP-ATR system  , T represents different temperatures in °K , and Kb is the binding constant. The calculated values for ΔH°, ΔS°, and ΔG° of the TYP-ATR system are given in The intermolecular acting forces, such as hydrophobic, electrostatic interactions, hydrogen bonds, and van der Waals interactions, exist between ligands and macromolecules . The theSynchronous fluorescence spectroscopy (SFS) is used to monitor alteration in the microenvironment of fluorophore amino acid residues . The synΔλ=60 nm . The SFS0 and F are fluorescence intensities of TYP in the absence and presence of ATR, respectively. To confirm the binding position of the ATR on the surface of TYP, the RSFQ ratio for the TYP-ATR system at tyrosine (The intensity of fluorescence of TYP (around tyrosine residue) decreased in the presence of ATR, and no shift in emission wavelength, confirming no change in the microenvironment around tyrosine residue A. Furtheequation D. As a rThe CD is a sensitive and powerful technique that explores the protein’s secondary structure, stability, and conformation. It provided information about perturbations in the secondary structure of TYP after treatment with ATR . The spee in TYP . Moreovee in TYP .−1 for C=O) and (Amide II band (1510–1580 cm−1 for C-N/N-H vibration)), which represents the secondary structure of TYP [−1 (Amide I) and 1544 to 1543 cm−1 (Amide II) in the ATR-TYP complexes, respectively. Thus, TYP interaction with amide I and II groups disturb the naturally occurring conformation and secondary structure of TYP [FT-IR spectroscopy is an efficient technique used in detecting changes in the secondary structure of the protein . Proteine of TYP . The FTIe of TYP . Althoug−1. In the binding pocket of TYP, the ATR molecule was surrounded by amino acid residues  of the TYP was evaluated at different concentrations of ATR, and it was observed that the TYP activity decreased with the addition of ATR A. The TYmax value decreased while the Km values almost remained unchanged on interaction with higher concentrations of ATR. Further, the TYP-ATR complex formation shows a non-competitive inhibition. In addition, the catalytic constant kcat and kcat/Km values decreased gradually at higher concentrations of ATR [However, Michaelis-Menten curves B of TYP s of ATR . Thus, tThis study provides a detailed binding mechanism of the TYP-ATR system and its effects on the structure conformations and the influence of these conformational changes on the enzymatic activities of the TYP. The results suggest a moderate binding interaction between TYP and ATR, and the binding strength was temperature-dependent. The molecular docking and thermodynamic results suggest the involvement of hydrophobic interaction and hydrogen bonds between TYP and ATR and a single binding site for ATR on TYP. In the TYP-ATR system, the TYP activity was inhibited with ATR in a concentration-dependent manner since ATR was bound to the catalytic site. This study provides a deeper understanding of the mechanism of binding of ATR with the digestive proteases from the point of view of toxicological effects.